word2VecTF.py

# Word2Vec
# May 30, 2015, 1455 hrs
# Vijay Prakash Dwivedi (mail@vijaydwivedi.com.np)

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random

import numpy as np
from six.moves import xrange

import tensorflow as tf

os.system('clear')
print("#----------------- wor2vec implementation in TensorFlow ----------------#")

# -------------------------------------------- #
# STEP 1: Read the data into a list of strings
def read_data():
	# Read the data file as a list of words
	with open ("/home/vijay321/Desktop/IASNLP/text8", "r") as myfile:
		read_data = myfile.readlines()

	return read_data[0].split()

words = read_data()
print('Data size', len(words))


# ------------------------------------------------------------------ #
# STEP 2: Build the dictionary and replace rare words with UNK token
vocabulary_size = 50000

def build_dataset(words, vocabulary_size):
	count = [['UNK', -1]]
	count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
	dictionary = dict()

	for word, _ in count:
		dictionary[word] = len(dictionary)	# here ranking (index) is done... Eg. dictionary['the'] = 1

	data = list()
	unk_count = 0

	for word in words:
		if word in dictionary:
			index = dictionary[word]
		else:
			index = 0	# dictionary['UNK']
			unk_count += 1
		data.append(index)

	count[0][1] = unk_count
	reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))

	return data, count, dictionary, reverse_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(words, vocabulary_size)
del words	# To reduce memory

print('Most common words(+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0


# -------------------------------------------------------------------- #
# STEP 3: Function to generate a training batch for the skip-gram model
def generate_batch(batch_size, num_skips, skip_window):
	global data_index
	assert batch_size % num_skips == 0
	assert num_skips <= 2 * skip_window

	batch = np.ndarray(shape=(batch_size), dtype=np.int32)
	labels = np.ndarray(shape=(batch_size,1), dtype=np.int32)

	span = 2 * skip_window + 1	# [ skip_window target skip_window ]
	buffer = collections.deque(maxlen=span)

	for _ in range(span):
		buffer.append(data[data_index])
		data_index = (data_index + 1) % len(data)

	for i in range(batch_size // num_skips):
		target = skip_window	# target label at the center of the buffer
		targets_to_avoid = [skip_window]

		for j in range(num_skips):
			while target in targets_to_avoid:
				target = random.randint(0, span-1)
			targets_to_avoid.append(target)
			batch[i * num_skips + j] = buffer[skip_window]
			labels[i * num_skips + j, 0] = buffer[target]

		buffer.append(data[data_index])

		data_index = (data_index + 1) % len(data)

	# Backtrack a little bit to avoid skipping words in the end of a batch
	data_index = (data_index + len(data) - span) % len(data)

	return batch, labels

batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)

for i in range(8):
	print(batch[i], reverse_dictionary[batch[i]],
		'->', labels[i, 0], reverse_dictionary[labels[i, 0]])


# ------------------------------------------ #
# STEP 4: Build and train a skip-gram model
batch_size = 128
embedding_size = 128	# Dimension of the embedding vector
skip_window = 1			# How many words to consider left and right
num_skips = 2			# How many times to reuse an input to generate a label

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16			# Random set of words to evaluate similarity on
valid_window = 100		# Only pick dev samples in the head of the distribution
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
# print(valid_examples)
num_sampled = 64

graph = tf.Graph()

with graph.as_default():

	# Input data
	train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
	train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
	valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

	# CPU Implementation
	with tf.device('/cpu:0'):
		# Look up embedding for inputs
		embeddings = tf.Variable(tf.random_normal([vocabulary_size, embedding_size], -1.0, 1.0))
		embed = tf.nn.embedding_lookup(embeddings, train_inputs)

		nce_weights = tf.Variable(tf.truncated_normal(
			[vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size)))
		nce_biases = tf.Variable(tf.zeros([vocabulary_size]))


	# Compute the average NCE loss for the batch
	# tf.nce_loss automatically draws a new sample of the negative labels each time we evaluate the loss
	loss = tf.reduce_mean(
		tf.nn.nce_loss(
			weights=nce_weights,
			biases=nce_biases,
			labels=train_labels,
			inputs=embed,
			num_sampled=num_sampled,
			num_classes=vocabulary_size))

	# Construct the SGD optimizer using a learning rate of 1.0
	optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

	# Compute the cosine similarity between minibatch examples and all embeddings
	norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
	normalized_embeddings = embeddings / norm
	valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)
	similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)

	# Add variable initializer
	# init = tf.global_variables_initializer()
	init = tf.initialize_all_variables()


# ------------------------------------------ #
# STEP 5: Begin training
num_steps = 10001

with tf.Session(graph=graph) as session:
	# We must initialize all Variables before we use them
	init.run()
	print("Initialized")

	average_loss = 0

	for step in xrange(num_steps):
		batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
		feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

		# We perform one update step by evaluating the optimizer op (including it
    	# in the list of returned values for session.run()
		_, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
		average_loss += loss_val

		if step % 2000 == 0:
			if step > 0:
				average_loss /= 2000
			# The average loss is an estimate of the loss over the last 2000 batches
			print("Average loss at step", step, ": ", average_loss)
			average_loss = 0

			# Note that this is expensive (~20% slowdown if computed every 500 steps)
			if step % 10000 == 0:
				sim = similarity.eval()

				for i in xrange(valid_size):
					valid_word = reverse_dictionary[valid_examples[i]]
					print(valid_word)
					top_k = 8		# number of nearest neighbours
					nearest = (-sim[i, :]).argsort()[1:top_k + 1]
					log_str = "Nearest to %s:" % valid_word

					for k in xrange(top_k):
						close_word = reverse_dictionary[nearest[k]]
						log_str = "%s %s," % (log_str, close_word)

					print(log_str)

	final_embeddings = normalized_embeddings.eval()

	print(len(embeddings.eval()))
	print(valid_embeddings)

# ------------------------------------------ #
# STEP 6: Visualize the embeddings
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
	assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
	plt.figure(figsize=(18, 18))	# In Inches

	for i, label in enumerate(labels):
		x, y = low_dim_embs[i, :]
		plt.scatter(x, y)
		plt.annotate(label,
					xy=(x,y),
					xytext=(5,2),
					textcoords='offset points',
					ha='right',
					va='bottom')

	plt.savefig(filename)

try:
	from sklearn.manifold import TSNE
	import matplotlib.pyplot as plt

	tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
	plot_only = 500
	low_dim_embs= tsne.fit_transform(final_embeddings[:plot_only, :])
	labels = [reverse_dictionary[i] for i in xrange(plot_only)]
	plot_with_labels(low_dim_embs, labels)

except ImportError:
	print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")